Aida de Haro-García

29 papers C 2Journal 22Unranked 4
YearRankTypeTitle / Venue / Authors
2026 J jnl
False flag: An evolutionary false labeling approach for multilabel classification.
Appl. Soft Comput.
Nicolás García-Pedrajas, José Manuel Cuevas-Muñoz, Manuel Mendoza-Hurtado, Javier Pérez-Rodríguez, Aida de Haro-García
2025 J jnl
Label repopulation through evolutionary computation for addressing challenging multilabel problems.
Appl. Soft Comput.
Nicolás García-Pedrajas, Juan Antonio Romero del Castillo, Aida de Haro-García
2024 J jnl
A thorough experimental comparison of multilabel methods for classification performance.
Pattern Recognit.
Nicolás García-Pedrajas, José Manuel Cuevas-Muñoz, Gonzalo Cerruela García, Aida de Haro-García
2024 J jnl
Evolutionary simultaneous under and oversampling of instances for dealing with class-imbalance datasets in multilabel problems.
Appl. Soft Comput.
Nicolás García-Pedrajas, José Manuel Cuevas-Muñoz, Aida de Haro-García
2024 J jnl
Extensive experimental comparison among multilabel methods focused on ranking performance.
Inf. Sci.
Nicolás García-Pedrajas, José Manuel Cuevas-Muñoz, Gonzalo Cerruela García, Aida de Haro-García
2023 J jnl
PARIS: Partial instance and training set selection. A new scalable approach to multi-label classification.
Inf. Fusion
Nicolás García-Pedrajas, José Manuel Cuevas-Muñoz, Juan Antonio Romero del Castillo, Aida de Haro-García
2022 J jnl
Grab'Em: A Novel Graph-Based Method for Combining Feature Subset Selectors.
IEEE Trans. Cybern.
Aida de Haro-García, José Pérez-Parras Toledano, Gonzalo Cerruela García, Nicolás García-Pedrajas
2021 J jnl
Floating Search Methodology for Combining Classification Models for Site Recognition in DNA Sequences.
IEEE ACM Trans. Comput. Biol. Bioinform.
Javier Pérez-Rodríguez, Aida de Haro-García, Nicolás García-Pedrajas
2020 J jnl
Ensembles of feature selectors for dealing with class-imbalanced datasets: A proposal and comparative study.
Inf. Sci.
Aida de Haro-García, Gonzalo Cerruela García, Nicolás García-Pedrajas
2020 J jnl
Influence of feature rankers in the construction of molecular activity prediction models.
J. Comput. Aided Mol. Des.
Gonzalo Cerruela García, José Pérez-Parras Toledano, Aida de Haro-García, Nicolás García-Pedrajas
2019 J jnl
Improving the combination of results in the ensembles of prototype selectors.
Neural Networks
Gonzalo Cerruela García, Aida de Haro-García, José Pérez-Parras Toledano, Nicolás García-Pedrajas
2019 J jnl
Instance selection based on boosting for instance-based learners.
Pattern Recognit.
Aida de Haro-García, Gonzalo Cerruela García, Nicolás García-Pedrajas
2018 J jnl
A general framework for boosting feature subset selection algorithms.
Inf. Fusion
Javier Pérez-Rodríguez, Aida de Haro-García, Juan Antonio Romero del Castillo, Nicolás García-Pedrajas
2018 J jnl
Combining three strategies for evolutionary instance selection for instance-based learning.
Swarm Evol. Comput.
Aida de Haro-García, Javier Pérez-Rodríguez, Nicolás García-Pedrajas
2014 J jnl
A Scalable Memetic Algorithm for Simultaneous Instance and Feature Selection.
Evol. Comput.
Nicolás García-Pedrajas, Aida de Haro-García, Javier Pérez-Rodríguez
2014 J jnl
Boosting instance selection algorithms.
Knowl. Based Syst.
Nicolás García-Pedrajas, Aida de Haro-García
2013 J jnl
A scalable approach to simultaneous evolutionary instance and feature selection.
Inf. Sci.
Nicolás García-Pedrajas, Aida de Haro-García, Javier Pérez-Rodríguez
2013 J jnl
OligoIS: Scalable Instance Selection for Class-Imbalanced Data Sets.
IEEE Trans. Cybern.
Nicolás García-Pedrajas, Javier Pérez-Rodríguez, Aida de Haro-García
2012 — ch.
A Scalable Feature Selection Method to Improve the Analysis of Microarrays.
Modern Advances in Intelligent Systems and Tools
Aida de Haro-García, Javier Pérez-Rodríguez, Nicolás García-Pedrajas
2012 J jnl
Large scale instance selection by means of federal instance selection.
Data Knowl. Eng.
Aida de Haro-García, Nicolás García-Pedrajas, Juan Antonio Romero del Castillo
2012 J jnl
Scaling up data mining algorithms: review and taxonomy.
Prog. Artif. Intell.
Nicolás García-Pedrajas, Aida de Haro-García
2011 — conf
A Comparison of Two Strategies for Scaling Up Instance Selection in Huge Datasets.
CAEPIA
Aida de Haro-García, Javier Pérez-Rodríguez, Nicolás García-Pedrajas
2011 C conf
A scalable method for instance selection for class-imbalance datasets.
ISDA
Aida de Haro-García, Nicolás García-Pedrajas
2011 — conf
Feature Selection for Translation Initiation Site Recognition.
IEA/AIE (2)
Aida de Haro-García, Javier Pérez-Rodríguez, Nicolás García-Pedrajas
2011 — conf
Instance Selection for Class Imbalanced Problems by Means of Selecting Instances More than Once.
CAEPIA
Javier Pérez-Rodríguez, Aida de Haro-García, Nicolás García-Pedrajas
2010 J jnl
Democratic instance selection: A linear complexity instance selection algorithm based on classifier ensemble concepts.
Artif. Intell.
César Ignacio García-Osorio, Aida de Haro-García, Nicolás García-Pedrajas
2010 C conf
Large Scale Instance Selection by Means of a Parallel Algorithm.
IDEAL
Aida de Haro-García, Juan Antonio Romero del Castillo, Nicolás García-Pedrajas
2010 — conf
Scaling Up Feature Selection by Means of Democratization.
IEA/AIE (2)
Aida de Haro-García, Nicolás García-Pedrajas
2009 J jnl
A divide-and-conquer recursive approach for scaling up instance selection algorithms.
Data Min. Knowl. Discov.
Aida de Haro-García, Nicolás García-Pedrajas